Language understanding applications (e.g., digital assistant applications) require at least some contextual language understanding for interpreting spoken language input. In this regard, digital assistant applications may have experience interpreting spoken language inputs having a specific domain and/or task. For example, a digital assistant application may provide accurate results when interpreting a spoken language input related to a calendar event. However, in scenarios where the digital assistant application does not know how to handle the spoken language input, a backend solution (e.g., the web) may be used to provide a user with results. It may be difficult to determine when to use the digital assistant application and when to use a backend solution for a given spoken language input. In some cases, deterministic hard-coded rules may be used to determine when to use the digital assistant application and when to use a backend solution to fulfill a user's request. The cost of crafting and implementing these rules, as well as evaluating their accuracy, is high. Additionally, hard-coded rules do not scale well for locale expansion (e.g., interpreting new and/or different languages). Furthermore, when it is determined to use a backend solution, the spoken language input is sent to the backend solution “as is” and a result is provided based on the received spoken language input. Consequently, as commonly known to the community, the hard-coded rules are “coarse-grained” and the overall user experience suboptimal.
It is with respect to these and other general considerations that embodiments have been made. Also, although relatively specific problems have been discussed, it should be understood that the embodiments should not be limited to solving the specific problems identified in the background.